An improved algorithm for missing data in wireless sensor networks

In the field of wireless sensor networks, missing data has been an issue of widespread concern. In this paper, an improved algorithm based on different data types, is proposed. Missing data is estimated by a new algorithm ,which is based on a variety of data types. Data, which is dynamic and transitional, is a correlative of missing data. And the multiple linear regression model is used to solve the problem of missing data whose type of value is different from the others. The proposed algorithm can not only ensures the normal data transmission, but also improves the traditional methods in dealing with the problem of the incompleteness of multiple data. Furthermore, the proposed algorithm avoids the limitations of the traditional methods for estimating real-time data.